Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 102
... shown in Fig . 6.5 . The arrows indicate the positive sides of the lines . In this figure is shown the history of weight - vector adjustments produced by presenting the patterns in the order Y1 , Y2 , Y3 , Y1 , Y2 , Y3 , Y1 , Y2 , Y3 ...
... shown in Fig . 6.5 . The arrows indicate the positive sides of the lines . In this figure is shown the history of weight - vector adjustments produced by presenting the patterns in the order Y1 , Y2 , Y3 , Y1 , Y2 , Y3 , Y1 , Y2 , Y3 ...
Sivu 103
... shown in Fig . 6.5 . If it were made too long , it would never be among the weight vectors closest to the hyperplanes which it must eventually cross . Therefore , it would never be adjusted , and W1 and W3 would wander around ...
... shown in Fig . 6.5 . If it were made too long , it would never be among the weight vectors closest to the hyperplanes which it must eventually cross . Therefore , it would never be adjusted , and W1 and W3 would wander around ...
Sivu 108
... shown in Fig . 6.8 . Note that the partition shown in Fig . 6.7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) ...
... shown in Fig . 6.8 . Note that the partition shown in Fig . 6.7a is also nonredundant . A nonredundant partition is not necessarily one that uses a minimum number of hyperplanes , however . Thus in Fig . 6 · 8a , one hyperplane ( line ) ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |